Search Results - (( _ evacuation system algorithm ) OR ( parameter optimization strategy algorithm ))*
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Modelling Autonomous Evacuation Navigation System (AENS) for optimal route using Dijkstra's algorithm
Published 2016“…Moreover, fire detection system and evacuation system operate independently whereby fire alarm control panel system (FACPS) only receives the signal and thereafter, the evacuation process takes parts without any information about the hazardous fire location. …”
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On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
Published 2015“…Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. …”
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The banjir rescue: an IoT-based flood evacuation center system / Nur Aliah Syamsurial
Published 2020“…Finally, the selection is made where the algorithm will choose the nearest evacuation center to the victims. …”
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Adaptive emergency evacuation centre management for dynamic relocation of flood victims using firefly algorithm
Published 2016“…Therefore, this article proposes a decision aid model to be utilized in realizing an adaptive emergency evacuation centre management system. This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app. …”
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The application of firefly algorithm in an adaptive emergency evacuation centre management (AEECM) for dynamic relocation of flood victims
Published 2016“…This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app.The proposed model operates using Firefly multi-objective optimization algorithm that creates an optimal schedule for the relocation of victims and resources for an evacuation centre.The proposed decision aid model and the adaptive system can be applied in supporting the National Security Council’s respond mechanisms for handling disaster management level II (State level) especially in providing better management of the flood evacuating centres.…”
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Banjir rescue: IOT-based flood evacuation center / Nor Azimah Khalid ... [et al.]
Published 2025“…Finally, the selection is made where the algorithm will choose the nearest evacuation center to the victims. …”
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Modification of dijkstra's algorithm for safest and shortest path during emergency evacuation
Published 2015“…Intelligent autonomous evacuation navigation (AEN) system manages to solve wayfinding problems during emergency evacuation, especially for those who are not familiar with the building. …”
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A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites
Published 2019“…Owing to its proven performance in many other optimization problems, the adoption of the parameter-free Teaching Learning-based Optimization (TLBO) algorithm as a new t-way strategy is deemed useful. …”
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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Published 2020“…Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. …”
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Finite impulse response optimizers for solving optimization problems
Published 2019“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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Finite impulse response optimizers for solving optimization problems
Published 2019“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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Enhanced multi-agent approaches for efficient evacuation and rescue operations in managing disasters
Published 2025“…This study addresses disaster management within Multi-Agent System (MAS) environments, focusing on two critical phases: evacuation and rescue. …”
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The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo...
Published 2025“…The Crossover Probability Factor (CPF) as the Certain Ratio (CR) and the Mutation Factor (MF) of the algorithm are gradually altered during algorithm iteration to enhance the method's performance during optimization. …”
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Migrating Birds Optimization based Strategies for Pairwise Testing
Published 2015“…For pairwise testing, test cases are designed to cover all possible pair combinations of input parameter values at least once. In this paper, we investigate the adoption of Migrating Birds Optimization (MBO) algorithm as a strategy to find an optimal solution for pairwise test data reduction. …”
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Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem
Published 2023“…Other metaheuristic approaches such as genetic algorithm, differential evolution algorithm, particle swarm optimization, and ant colony optimization are still preferable to address combinatorial optimization problems. …”
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Sequence and sequence-less t-way test suite generation strategy based on the elitist flower pollination algorithm
Published 2018“…In line with the emerging field called Search based Software Engineering, many recently developed t-way strategies have adopted meta-heuristic algorithms as the basis of their implementations such as Simulated Annealing, Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization, Harmony Search and Cuckoo Search, owing their superior performance in term of test size reduction as compared to general computational based strategies, such as General t-way, Test Vector Generator, In Parameter Order General, Jenny, and Automatic Efficient Test Generator. …”
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Hybrid Migrating Birds Optimization Strategy for t-way Test Suite Generation
Published 2019“…This paper presents the implementation of meta-heuristic search algorithms that are Migrating Birds Optimization (MBO) algorithm and Genetic Algorithm (GA) hybrid to a t-way test data generation strategy. …”
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Nature-inspired parameter controllers for ACO-based reactive search
Published 2015“…This study proposes machine learning strategies to control the parameter adaptation in ant colony optimization algorithm, the prominent swarm intelligence metaheuristic.The sensitivity to parameters’ selection is one of the main limitations within the swarm intelligence algorithms when solving combinatorial problems.These parameters are often tuned manually by algorithm experts to a set that seems to work well for the problem under study, a standard set from the literature or using off-line parameter tuning procedures. …”
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